Representation Learning for Users' Web Browsing Sequences

Yukihiro TAGAMI  Hayato KOBAYASHI  Shingo ONO  Akira TAJIMA  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.7   pp.1870-1879
Publication Date: 2018/07/01
Publicized: 2018/04/20
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7335
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
online advertising,  Web browsing behavior,  Paragraph Vector,  representation learning,  

Full Text: PDF(864.2KB)>>
Buy this Article

Modeling user activities on the Web is a key problem for various Web services, such as news article recommendation and ad click prediction. In our work-in-progress paper[1], we introduced an approach that summarizes each sequence of user Web page visits using Paragraph Vector[3], considering users and URLs as paragraphs and words, respectively. The learned user representations are used among the user-related prediction tasks in common. In this paper, on the basis of analysis of our Web page visit data, we propose Backward PV-DM, which is a modified version of Paragraph Vector. We show experimental results on two ad-related data sets based on logs from Web services of Yahoo! JAPAN. Our proposed method achieved better results than those of existing vector models.